Regression Models for Forecasting Global Oil Production

Aydin G.

PETROLEUM SCIENCE AND TECHNOLOGY, cilt.33, ss.1822-1828, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1080/10916466.2015.1101474
  • Dergi Adı: PETROLEUM SCIENCE AND TECHNOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1822-1828
  • Anahtar Kelimeler: oil, production, regression analysis, modeling, forecasting, NATURAL-GAS, ENERGY-CONSUMPTION
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

In this study, the global oil production is modeled in order to forecast future projections based on the historical trend using the linear and nonlinear regression analysis. The developed models are validated considering the behavior of determination coefficient, F, and t tests. Forecasting performances of the proposed models are also compared using mean absolute percentage error. The results show that proposed models can be effectively used for forecasting the global oil production. The compared results show that the inverse regression model gives the best forecasting performance. It forecast the global oil production as 4593 Mt in 2020, 8.84% higher than the level in 2014.